As manufacturing and processing requirements become more and more complex, today's plants and other manufacturing and processing facilities contain more and more machines and other complex mechanical components and devices of all sizes and shapes and for an exceedingly large variety of applications. For example, in a typical petroleum refinery or other chemical plant, hundreds or even thousands of machines may exist in connection with the various processes being performed at the particular facility.
These machines may include compressors, turbines, pumps, motors, fans and other devices that employ some manner of rotation in connection with their operation. In order to maintain, troubleshoot and otherwise operate these machines over time, it is often important to obtain relatively frequent RPM (rotations per minute) readings with respect to the rotational elements of the machines. These RPM readings can be used to diagnose many problems with the machines that are not readily apparent to the naked eye or are otherwise difficult or impossible to ascertain without the aid of the RPM readings. For example, significant deviations in RPM speed from that which is called for in the machine specification may indicate the existence of an operational problem. Also, significant deviation from the past characteristic operating RPM speed for a particular machine may signal that some form of maintenance or repair is required. As yet another example, known operational problems may be suspected based upon vibration information as the vibration frequency spectrum of the machine relates to the rotational speed of the machine. The presence of excessive vibration levels at certain frequencies, known as defect or fault frequencies, usually indicates a specific machine fault or operational problem. For example, a high vibration at a frequency of 1×RPM may be caused by an unbalance of the machine shaft. The defect frequencies are directly related to the machine speed as multiples of RPM.
In order to properly make such diagnoses, it is quite important for the RPM readings to be accurate, because improper or inaccurate RPM readings can lead to the false belief that a problem exists when one actually does not or, alternatively, the false belief that a problem does not exist when, in fact, one actually does. Additionally, inaccurate RPM readings can lead to misdiagnosis of a machine problem. High accuracy of RPM readings is particularly important when high frequency vibration components are used to detect problems associated with rotating elements of bearings because a small error in RPM readings will be amplified at high frequencies.
There are various prior art methods for obtaining RPM readings for rotating machines. One common technique is to directly measure rotational speed by installing an RPM sensor, commonly known as a “Key Phaser” or a “Tachometer”, on the machines. Unfortunately, these RPM sensors are quite difficult to install on existing machines. Further, the sensors are quite expensive and given the large numbers of machines in typical plants, which can number in the thousands, the costs can be prohibitive. It is for this reason that direct speed sensor measurement is often limited to a few critical machines such as major process compressors in a refinery application.
Another method for obtaining RPM readings which is currently in use is through a high-resolution Fast Fourier Transform (FFT) analysis of the vibration signals in order to arrive at an estimate for the RPM value. This method, however, typically requires an operator to interpret the FFT spectrum and is not, therefore, suitable for automatic on-line vibration analysis. Notwithstanding this, as low-cost data acquisition systems are being made commercially available on a broader basis, plants have begun to implement on-line vibration monitoring systems on machines which are not mission critical such as, for example, pumps and motors. These vibration monitoring systems are usually equipped with only vibration sensors and not with speed sensors because of cost constraints. Further, the majority of low-cost on-line vibration monitoring systems are not capable of providing high-resolution FFT vibration analysis. Without direct RPM sensors, the vibration monitoring systems currently in use are relatively inaccurate in terms of providing RPM readings.
There have been attempts to provide more accurate RPM readings based upon vibration analysis techniques. However, many of these techniques still suffer from drawbacks including inaccuracy in RPM readings. In particular, these techniques often result in inaccurate readings especially when the noise associated with the vibration readings is high—a common situation in most plant applications. For example, M. D. Hicho discloses a method for determining the RPM of a rotating machine from a vibration frequency spectrum (see U.S. Pat. Nos. 5,109,700 and 5,115,671, Method and Apparatus for Analyzing Rotating Machines). Hicho's method identifies a set of vibration peaks out of a measured vibration frequency spectrum that corresponds to the frequencies of 1×, 2× and/or 3× of the target RPM of the machine to be measured, and uses those frequencies to estimate the RPM.
This method is simple and straightforward. However, the accuracy of the method is limited to the frequency resolution, amplitude accuracy and background noise in the vibration frequency spectrum. The FFT technique employed to obtain the vibration frequency spectrum is inherently inaccurate due to the spectrum smearing or energy leakage in determining the true peak values of the vibration. Many low-cost data acquisition systems can only provide relatively low resolution of the FFT spectrum. In addition, the FFT technique of Hicho's method neglects the essential phase information of the vibration components and is not effective in suppressing the random noise when compared with averaging techniques in time domain such as “synchronous averaging”. Another difficulty with this method is that the selection of a criterion to identify the peaks from the vibration frequency spectrum is arbitrary.
Another method of estimating the RPM of a rotating machine from vibration data is disclosed by K. R. Piety (U.S. Pat. No. 5,744,072, Method for Determining Rotational Speed from Machine Vibration Data). This method compares the measured vibration frequency spectrum of an unknown machine RPM with a reference spectrum of a known RPM from the same machine, derives a spectrum stretch factor that provides optimal correlation between the two spectra, and determines the RPM of the machine from the stretch factor and the known RPM of the reference spectrum. This method has the same limitations as Hitcho's method because it also operates on the FFT spectrum.
While it is important to obtain an accurate reading for RPM in connection with condition monitoring, RPM determination is merely one, albeit an important, step in the overall process. With various advances in information technology and new applications becoming available all of the time, the migration to on-line and coordinated monitoring of large groups of equipment is not surprising. In fact, given these improvements and new capabilities, the practice of machine maintenance has trended away from time-based solutions wherein machines are tested at particular time intervals and towards condition-based solutions wherein testing and problem resolution occurs only upon detection of a problem or a suspected problem.
In this way, the number of unnecessary machine servicing and testing sessions and shutdowns and their associated costs are greatly reduced. But perhaps even more importantly, costly machine breakdowns can be reduced or even eliminated in some cases due to the ability to detect faults earlier before they can do much damage.
Currently, the most widely used techniques to monitor the condition of rotating machines employ vibration measurements. Most machines have a typical vibration level and a frequency spectrum with a characteristic shape when the machine is in good condition. If a machine fault develops, the dynamic processes in the machine change and some of the forces acting on and within the machine therefore change. As a result, the vibration level and shape of the vibration spectrum changes. By monitoring the change in the vibration level and spectrum shape, a trained machine operator is usually able to detect not only the presence of a machine fault but also, in many cases, the type of fault present.
As the cost of data acquisition systems and computers is continually reduced, on-line vibration monitoring systems are becoming more and more popular as replacements for conventional portable measurement solutions throughout continuous process industries. The availability of on-line and historical vibration data provides operators with a means for continuously monitoring machine condition. However, at the same time, these machine operators and service personnel are subject to data overload given these new systems. Therefore, some level of automated data analysis is desirable so that attention may be focused on only those situations that require the same.